Method of Analyzing the Brain Activity of a Subject

Abstract

The invention concerns a method of analysing the brain activity of a patient performing a given task or in response to an external stimulus, by comparison of standardized data with data in a database, by means of fuzzy logic algorithms.

Claims

1. A method for generating normalized data that can be used for the implementation of a method as claimed in one of claims 1 to 4, in which said data comprise, for each temporal acquisition block (t1) of the data during the performance of the task or the application of the stimulus, the protocol for performing said task or for applying said stimulus representing a paradigm: a. of the functional brain data of the grey matter obtained during the performance of said cognitive task or after application of the stimulus b. of the structural data of the white matter fibers linking the grey matter areas, obtained during the performance of said cognitive task or after application of the stimulus c. of the anatomical data of the brain of said subject, said method comprising the steps of i. normalization of said anatomical data, in order to represent the brain of the subject in a normalized coordinate ii. normalization of said functional data on the basis of a normalized functional atlas representing the cortical areas and the central grey nuclei of the brain, said functional atlas being in the same normalized coordinate as the atlas of (i) iii. normalization of said structural data of the white matter fibers on the basis of said anatomical atlas of (ii) iv. for each temporal block (t1), searching, voxel by voxel, for the activated functional brain areas, and for the bundles of white matter fibers uniting each of these activated brain areas v. for each of the functional brain areas studied, averaging of the correlation coefficients of each of the voxels of said area with the paradigm, in order to obtain a unique correlation coefficient of said area with the paradigm said normalized data obtained on completion of the step v thus consisting of: for each temporal block (t1) normalized data representing the white matter fibers of the brain of the subject normalized data representing the correlation coefficient of each functional area (grey matter) with the paradigm (performance of the task or application of the stimulus) definition of the task performed or of the stimulus applied.

2. The method as claimed in claim 2, further comprising a step of increasing the temporal resolution between each temporal acquisition block (t1), by dividing into equal parts (interpolated temporal blocks (t2)) the time between two temporal acquisition blocks (t1) and interpolating the statistically significant signal variations at the level of each voxel acquired for the functional data by using a generalized linear model, said step being performed between the step (iii) of normalization of the structural data and the step (iv) of searching, voxel by voxel, for the activated functional brain areas, and for the bundles of white matter fibers uniting each of these activated brain areas, said step (iv) being then performed for each interpolated temporal block (t2), said normalized data obtained on completion of the step v thus consisting of: for each temporal block (t2) d. normalized data representing the white matter fibers of the brain of the subject e. normalized data representing the correlation coefficient of each functional area (grey matter) with the paradigm (performance of the task or application of the stimulus) f. definition of the task performed or of the stimulus applied.

3. The method as claimed in claim 1 or 2, characterized in that said functional brain areas studied in the step v are only the areas for which a voxel-by-voxel search has identified that they are activated.

4. The method as claimed in one of claims 1 to 3, further comprising a step of averaging of the results of the temporal blocks corresponding to the paradigm, said step making it possible to obtain normalized data consisting of normalized data representing the white matter fibers of the brain of the subject normalized data representing the correlation coefficient of each functional area (grey matter) with the paradigm (performance of the task or application of the stimulus) definition of the task performed or of the stimulus applied.

5. The method as claimed in one of claims 1 to 4, further comprising a storage of said data within a database.

6. The method as claimed in claim 5, characterized in that, upon the storage of said data in the base, a weighting coefficient is assigned to said data, computed by using a fuzzy logic algorithm, by comparing said data to be stored to those already contained in the base, for said task or said stimulus.

7. A method for analyzing the brain activity of a subject during the performance of a task or in response to a stimulus comprising the steps of normalization of data (d1) collected during the performance of said task or application of said stimulus in order to obtain normalized data (d2), by implementation of the method as claimed in one of claims 1 to 6, comparison of said normalized data (d2) with data (d3) present in a normalized database said data (d3) of said database each being specific to a task of a given stimulus, said comparison being performed by a fuzzy logic algorithm, said method making it possible to determine a degree of similarity of said normalized data (d2) with data present in the normalized database, said method making it possible to determine the brain activity of said subject during the performance of said task or in response to said stimulus.

8. The method as claimed in claim 7, characterized in that said data (d1) collected during the performance of the task or the application of the stimulus are MRI, PET scanner, echography, or electroencephalography (EEG) data.

9. The use of the method as claimed in one of claim 7 or 8, in a comparative analysis of the state of wakefulness at rest among healthy subjects and among subjects who cannot communicate with the environment.

10. The use of the method as claimed in one of claim 7 or 8, in an analysis of psychiatric problems in a subject.

11. The use of the method as claimed in one of claim 7 or 8, in a clinical and functional evaluation of a deficiency or of a neurological problem in a subject.

12. The use of the method as claimed in one of claim 7 or 8, for detecting whether a subject tells the truth or lies in responding to different questions.

13. The use of the method as claimed in one of claim 7 or 8, in neuromarketing studies, for better understanding the reactions of subjects upon the presentation of new products, or the evocation of product development.

14. The use of the method as claimed in one of claim 7 or 8, characterized in that said stimulus is an absence of stimulus and that the patient is at rest (resting state).

Description

DESCRIPTION OF THE FIGURES

[0211] FIG. 1: list of the 116 grey matter elements that can be used in a normalized atlas

[0212] FIG. 2: list of the 58 bundles of white matter that can be used in a normalized atlas

[0213] FIG. 3: algorithm for obtaining normalized data and for comparison with a database. FL: fuzzy logic; SB: white matter; SG: grey matter; Fx: bundles; Fa: anisotropic fraction; Nb: number; Lg: length; diff stat sign: significant statistical difference.

EXAMPLES

Example 1

Data Acquisitions

[0214] 1.1 Acquisitions of the functional brain data of the grey matter (cortex, central grey nuclei) during the performance of a cognitive task (block paradigm) in activation functional MRI among healthy subjects: spatial resolution: 2 mm.sup.3; temporal resolution: 1.5 s

[0215] 1.2 Acquisition of the structural data of the white matter fibers linking the grey matter areas in diffusion tensor MRI (DT1 or HARDI technique); spatial resolution: 2 mm.sup.3

[0216] 1.3 Acquisition of the anatomical data in T1-weighted volume morphological MRI; spatial resolution: 2 mm.sup.3

[0217] 1.4 Paradigm used: 3 activation blocks in the paradigm with 360 inputs (acquisitions) per block

Example 2

Analysis of the Data Acquired

[0218] 2.1 Co-registration and normalization of the anatomical data on the T1 atlas of the MNI (spatial resolution: 1 mm.sup.3). After correction of the movement artefacts and of the spatial deformations due to the MRI acquisition methods used (echo planar), co-registration and normalization of the functional and structural data on the anatomical data of the subject already co-registered on the atlas of the MNI; spatial resolution of all the data: 1 mm.sup.3

[0219] 2.2 Functional data (grey matter): after new co-registration and spatial normalization of the data acquired on a specific anatomical atlas with 116 inputs (cortical areas and central grey nuclei, FIG. 1), analysis in pseudo-real time (temporal resolution of the acquisition interpolated with an algorithm, making it possible to switch from a resolution of 1.5 s to 150 ms) of the statistically significant signal variations at the level of each voxel acquired in activation fMRI during the task performed according to a block or event-driven paradigm, by using the statistical general linear model, and by normalizing the results in a coordinate with fixed temporal resolution (150 ms).

[0220] 2.3 Structural data (white matter): the data acquired in diffusion tensor already co-registered on the anatomical atlas used in 2.1 and 2.2 are again co-registered and normalized on an atlas of the specific white matter fibers comprising 58 bundles (details in FIG. 2). The extraction of the bundles of the subject studied is performed automatically by using a mid-deterministic, mid-probabilistic global tractography method.

[0221] The extraction is performed as follows: the atlas initiates the extraction algorithm by supplying the extraction departure areas (grains) for each of the 58 bundles considered, and by iteratively comparing the results of the extraction obtained (parameters analyzed: anisotropy fraction, bundle length, number of fibers) with the known values of the initial bundle in the atlas. The iterations are stopped when the statistical differences between these values are no longer significant, and/or when there is overlapping of bundles.

Example 3

Analysis of the Structural and Functional Connections: Establishment of the “GPS Maps” for the Brain Task Studied

[0222] A first analysis is performed per temporal block of 150 ms over the set of the blocks of the paradigm of the functional acquisition.

[0223] For each temporal block, the algorithm searches voxel by voxel, for the activated functional brain areas, and searches for the bundles of fibers uniting each of these activated brain areas. The mapping is established according to a Boolean model (activated, not activated) per cortical region, per white matter bundle, and per temporal block. The parameter for individualizing the activated areas is set according to a statistical threshold of r=0.3.

[0224] A statistical classification with weighted geometrical averaging of the results obtained within each temporal block is then performed by comparing the results obtained in each block of the paradigm with those of the other blocks of the paradigm so as to normalize the

[0225] “GPS” mapping thus created.

[0226] A reduction of the temporal dimension of this map is performed by averaging of the results of the temporal blocks by a factor 9.

[0227] In particular

[0228] Map dimensions and inputs:

[0229] SG=grey matter

[0230] SB=white matter

[0231] r: coefficient of correlation with the paradigm

[0232] 1.sup.st Analysis

[0233] X[nb SG areas]×Y[nb SB bundles]×Z[(nb activated paradigms*nb temporal blocks within the paradigm)]

[0234] For a paradigm of 3 activated paradigm blocks, and 360 temporal blocks of 150 ms within the paradigm:

[0235] X[116]×Y[58]×Z[3*360]

[0236] Each input at X[i], Y[j], Z[k*l] is retained if their r>0.3 (significance threshold), and not retained (set to the value=0) otherwise.

[0237] 2.sup.nd Analysis

[0238] Subdivision of X[116]×Y[58]×Z[3*360] into X[116]×Y[58]×Z[3,360] (over Z 3 temporal areas of 360 inputs). This step in fact corresponds to recognizing that, in a paradigm of 3 activated paradigm blocks of a duration T, there are three identical blocks present and switching from a state reflecting this duration T to three reflecting states each of a duration T/3, each corresponding to one of the activated paradigm blocks.

[0239] Comparison, classification and averaging between [0240] X[i], Y[j], Z[k,l] [0241] X[i], Y[j], Z[k,l+1] and [0242] X[i], Y[j], Z[k.l+2], with 0≦1≦2

[0243] Creation of a map X[i]. Y[j], Z[m] with m=k=average of the results between Z[k,l], Z[k.l+1] and Z[k,l+2]

[0244] 3. Reduction of the Dimensions of the Map

[0245] Transformation of the map X[116]×Y[58]×Z[3*360] into map X[116]×Y[58]×Z[360] (averaging between each block of the paradigm)

[0246] then into X[116]×Y[58]×Z[40] (reduction by a temporal factor 9)

[0247] by averaging of i=0 to 8 of the X[116]×Y[58]×Z[i]

[0248] 4. Boolean Normalization of the Map

[0249] The values are retained if r average >0.3, and are not retained otherwise.

Example 4

Recording of the Data in a Database

[0250] Each map established for a specific task is inserted into an input of the database, with an initial weighting coefficient set at 1.

[0251] The database comprises 3 inputs with different dimensions: [0252] n maps of a given cognitive task [0253] n weighting coefficients, and [0254] m numbers of different cognitive tasks.

[0255] On each new input into the base, if there are already one or more similar task maps inserted into the base, the weighting coefficient of the new map inserted is recomputed by using a fuzzy logic algorithm making it possible to compute the percentage similarity between the new task to be inserted into the base and the data already present.

[0256] The analysis of the weighting coefficient is performed by comparing the X[116]×Y[58]×Z[40] values of each map with one another and by determining their similarity threshold.

[0257] Thus, the more different inputs the database has for a task merit, the more accurate the weighting coefficient of the new input will be.

[0258] This database becomes increasingly relevant as it is enriched with new inputs.

Example 5

Use of the Reference Database

[0259] For each functional task performed by a subject (or patient), the map generated is compared to the inputs of the base by using the same fuzzy logic algorithm as that used for the computation of the weighting coefficients during the creation and the enrichment of the database.

[0260] It is possible to compare a specific task with the similar tasks already present in the database (e.g.: motricity of the left hand); the result of this comparison will then be a percentage similarity on the specific task studied.

[0261] It is possible to compare a specific task with different tasks already present in the database (e.g.:

[0262] motricity of the left hand versus the right hand; motricity of the mouth versus verbal fluency by category, etc.). There will then be a percentage similarity available which will take into account the areas common to two different tasks and a percentage mismatch which will take into account the brain areas not common to the two different tasks.